Resource-constrained Networks Research Articles

Wireless Federated Learning Over Resource-Constrained Networks: Digital Versus Analog Transmissions

Wireless Federated Learning Over Resource-Constrained Networks: Digital Versus Analog Transmissions

Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.

Local Load Migration in High-Capacity Fog Computing

Fog computing brings storage and computational capabilities closer to the data source, which reduces latency and enhances efficiency in processing data. However, these capabilities are resource-constrained at the fog nodes as compared to the cloud core. This limitation can result in high computational loads that yield in saturation and congestion at the fog nodes when processing large traffic volumes. Hence, this paper introduces a novel load migration scheme for delay-sensitive and computation-intensive requests in fog computing without relaying to the cloud core, thus avoiding prolonged link delays. First, a grouped service function chain (SFC) provisioning framework is embedded on a heterogeneous architecture composed of super fog (SF) and ordinary fog (OF) nodes, where all the virtual network functions (VNFs) in the SFC are mapped on a single SF node to reduce resource use. Here, the SF nodes are always in active mode to receive incoming traffic, whereas the OF nodes are maintained in idle mode to save power and computing resources. When the SF node approaches a saturation threshold alarm, it diffuses its load (hosted VNFs) gradually to neighboring OF nodes in the vicinity, thus avoiding saturation at the SF node and providing service continuity. The framework is implemented on a resource-constrained network to achieve realistic operating conditions. Overall, the proposed work achieves high admission and resource utilization rates, reduced delays, and power consumption, as compared to key network architectures and provisioning schemes that separately map delay-sensitive and computation-intensive on hierarchical fog layers, which incur increased delays, network traffic, and power usage.

A Novel Lightweight NIDS Framework for Detecting Anomalous Data Traffic in Contemporary Networks

Network Intrusion Detection Systems (NIDSs) have been proposed in the literature as security tools for detecting anomalous and intrusive network data traffic. However, the existing NIDS frameworks are computation-intensive, thereby making them unsuitable for deployment in resource-constrained networks with limited computational capabilities. This paper aims to address this issue by proposing computationally efficient NIDS framework for detecting anomalous data traffic in resource-constrained networks. The proposed NIDS framework uses an ensemble-based classifier model comprising multiple classifiers, which enables it to achieve high accuracy and detection rate across a wide range of low-footprint and stealth network attacks. The proposed framework also uses feature scaling and dimensionality reduction techniques to minimize the overall computational overhead. The proposed framework consists of two stages. In the first stage, four distinct base-level classifiers are utilized. The classification probabilities of the first stage are used in the modified meta-level classifier. The modified meta-level classifier is trained on the class probabilities of the base-level classifiers combined using a novel proposed probability function. The performance of the proposed NIDS framework is evaluated on a proprietary testbed dataset and two benchmark datasets namely CICIDS-2017 and UNSW-NB15. The results reveal that the proposed NIDS framework provides better performance than the existing NIDS frameworks in terms of false positive rate, despite using a significantly lower number of input features for its analysis.

Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks

Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks

Energy­efficient routing protocol in wireless sensor networks

Recently, self-organizing networks have gained popularity, which are distinguished by the possibility of connecting terminals anywhere and at any time without the need for a centralized network infrastructure. Mobile self-organizing networks are a promising direction for the development of information and communication systems. However, one should take into account the problems arising from the very nature of such networks: mobile nodes lead to an increase in the dynamism of the network topology due to the possibility of disconnection and the probability of movement; the limited supply of power sources of nodes requires careful management of power consumption in the design of hardware and protocols. Energy-saving design in self-organizing networks is more complex and important than in other wireless networks. The lack of infrastructure requires mobile nodes to act as routers, which increases traffic and therefore power consumption. Also, the energy-saving mode must take into account different network performance criteria, which can be caused by different protocols and power limitations. Thanks to the latest advances in technology and the rapid development of the production of small and affordable sensors, it has become possible to use them technically and economically effectively. These electronic sensors, which have high sensitivity, measure environmental parameters and convert the received data into an electronic signal that is transmitted over a wireless channel. After processing this signal, you can get information about the properties of objects or events that occur near the sensor. The application of a large number of such sensors, which can monitor a large area and exchange data wirelessly, forms a single wireless sensor network. Route allocation is a key challenge for resource-constrained networks. With the growth of computing power in wireless sensor technology, these sensor nodes are required to be equipped with more and more capabilities to perform more complex tasks. Each sensor is limited by its energy level, processing power, and ability to perceive information. Thus, the network of these sensors forms a more reliable and accurate structure. The article discusses the issue of using the HABRP protocol for its application in wireless sensor networks. The use of the HABRP protocol allows for improvement in the efficiency of wireless sensor networks, namely their energy efficiency. Therefore, the energy efficiency and simplicity of the HABRP protocol make it an excellent candidate for use in wireless sensor networks.

Design an Improved Trust-based Quality of Service AwareRouting in Cognitive Mobile Ad-Hoc Network

Mobile ad hoc networks (MANETs) are wireless networks that can be configured at will. It has no infrastructure and centralized control, so it is only suitable for provisional communications. In a dynamically topological and resource-constrained network, ensuring QoS and security is challenging. MANETs are dynamic networks, so navigating them can be challenging and more susceptible to attacks. MANET requires significant memory, speed, and transmission bandwidth for conventional security measures like cryptographic techniques. Consequently, these methods are unsuitable for identifying malicious behaviour or self-centered nodes. Nodes that are malicious, selfish, or malfunctioning can be identified based on the trust method, which calculates how much trust exists between them. A trust-based QOS-aware routing protocol is proposed in this paper to calculate trust in MANET (I-TQAR). The tree important performance metrics are considered for result validation such as delay, throughput and packet delivery ratio (PDR). I-TQAR offers significantly improved performance in all areas compared to the existing TQR and TQOR protocols.

UniPreCIS: A data preprocessing solution for collocated services on shared IoT

Next-generation smart city applications, attributed to the power of the Internet of Things (IoT) and Cyber–Physical Systems (CPS), significantly rely on sensing data quality. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-a-service these days, it is imperative that a shared sensing infrastructure could thwart the better utilization of resources. However, a shared sensing infrastructure that leverages low-cost sensing devices for a cost-effective solution remains unexplored territory. A significant research effort is still needed to make edge-based data shaping solutions more reliable, feature-rich, and cost-effective while addressing the associated challenges in sharing the sensing infrastructure among multiple collocated services with diverse Quality of Service (QoS) requirements. Towards this, we propose UniPreCIS, a novel edge-based data preprocessing solution that accounts for the inherent characteristics of low-cost ambient sensors and their exhibited measurement dynamics concerning application-specific QoS. UniPreCISaims to identify and select quality data sources by performing sensor ranking and selection that dynamically adapts to the change in sensor attributes. Finally, multimodal data preprocessing is performed in a unified manner to meet heterogeneous application QoS and, at the same time, reduce the resource consumption footprint for the resource-constrained network edge. We study the effectiveness of UniPreCISon a real-world testbed deployed on our campus. As observed, the processing time and memory utilization of the stakeholder services have been reduced in the proposed approach while achieving up to 90% accuracy, which is arguably significant compared to state-of-the-art sensing techniques.

QoS-Aware and Energy Data Management in Industrial IoT

Two crucial challenges in Industry 4.0 involve maintaining critical latency requirements for data access and ensuring efficient power consumption by field devices. Traditional centralized industrial networks that provide rudimentary data distribution capabilities may not be able to meet such stringent requirements. These requirements cannot be met later due to connection or node failures or extreme performance decadence. To address this problem, this paper focuses on resource-constrained networks of Internet of Things (IoT) systems, exploiting the presence of several more powerful nodes acting as distributed local data storage proxies for every IoT set. To increase the battery lifetime of the network, a number of nodes that are not included in data transmission or data storage are turned off. In this paper, we investigate the issue of maximizing network lifetime, and consider the restrictions on data access latency. For this purpose, data are cached distributively in proxy nodes, leading to a reduction in energy consumption and ultimately maximizing network lifetime. To address this problem, we introduce an energy-aware data management method (EDMM); with the goal of extending network lifetime, select IoT nodes are designated to save data distributively. Our proposed approach (1) makes sure that data access latency is underneath a specified threshold and (2) performs well with respect to network lifetime compared to an offline centralized heuristic algorithm.

Stabilization of linear discrete-time systems over resource-constrained networks under dynamical multiple description coding scheme

In this paper, the stabilization problem is investigated for a class of linear discrete-time systems in a resource-constrained network environment where only the data with finite bits is permitted to transmit. The data transmissions from the sensors to the controller are subject to random packet dropouts. To improve the reliability of the data transmission, a novel dynamical multiple description coding scheme is developed with guaranteed convergence of the decoding error. By recurring to the matrix inequality theory and the stochastic analysis technique, sufficient conditions are established to ensure the stochastic stability of the closed-loop system. Furthermore, a parametric relationship is discovered that characterizes the influences from the packet-dropout probabilities and the encoder parameters on the stability of the closed-loop system. Finally, a numerical example is provided to illustrate the validity of the proposed control scheme.

Deep reinforcement learning‐based routing and resource assignment in quantum key distribution‐secured optical networks

AbstractIn quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resources limit the success probability of QKD lightpath requests (QLRs). Thus, the selection of an appropriate route and the efficient utilisation of network resources for establishment of QLRs are the essential and challenging problems. This work addresses the routing and resource assignment (RRA) problem in the quantum signal channel of QKD‐ONs. The RRA problem of QKD‐ONs is a complex decision making problem, where appropriate solutions depend on understanding the networking environment. Motivated by the recent advances in deep reinforcement learning (DRL) for complex problems and also because of its capability to learn directly from experiences, DRL is exploited to solve the RRA problem and a DRL‐based RRA scheme is proposed. The proposed scheme learns the optimal policy to select an appropriate route and assigns suitable network resources for establishment of QLRs by using deep neural networks. The performance of the proposed scheme is compared with the deep‐Q network (DQN) method and two baseline schemes, namely, first‐fit (FF) and random‐fit (RF) for two different networks, namely The National Science Foundation Network (NSFNET) and UBN24. Simulation results indicate that the proposed scheme reduces blocking by 7.19%, 10.11%, and 33.50% for NSFNET and 2.47%, 3.20%, and 19.60% for UBN24 and improves resource utilisation up to 3.40%, 4.33%, and 7.18% for NSFNET and 1.34%, 1.96%, and 6.44% for UBN24 as compared with DQN, FF, and RF, respectively.

Link quality and energy efficient optimal simplified cluster based routing scheme to enhance lifetime for wireless body area networks

Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable multi path data transfer. This work was executed in three phases. First, an optimal simplified clustering technique for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.

Cooperative IDS for Detecting Collaborative Attacks in RPL-AODV Protocol in Internet of Everything

Internet of everything (IoET) is one of the key integrators in Industry 4.0, which contributes to large-scale deployment of low-power and lossy (LLN) networks to connecting people, processes, data, and things. The RPL is one of the unique standardized routing protocols that enable efficient use of smart devices energy, compute resources to address the properties and constraints of LLN networks. The authors investigate the RPL-AODV routing protocol's performance in combining the advantages of both RPL and AODV routing protocol, which works together in a low power resource-constrained network. The main challenging issue is collaborating the AODV and RPL routing protocol in the LLN network. This paper also models the collaborative attacks such as wormhole, blackhole attack for AODV, and rank and sinkhole attacks to exploit the vulnerability of RPL protocol. Finally, the cooperative IDS combining specification-based and signature-based IDS is proposed to detect the collaborative attacks against the RPL-AODV routing protocol that effectively monitors and provides security to the LLN networks.

Performance analysis of entropy variation-based detection of DDoS attacks in IoT

Internet of Things (IoT) comprises of connected devices that collaborate to perform numerous tasks individually and collectively to facilitate services to the users. Distributed denial of service attacks aim to exhaust the resources of the target by sending enormous inessential traffic from numerous sources. IoT systems, generally being a resource-constrained network, are highly vulnerable to DDoS attacks. We aim to achieve the detection of DDoS attacks by rule-based detection method using information theory metrics. Variation in entropy of traffic features beyond a threshold is an indicator of change in density of traffic to a network. In this paper, we present a performance analysis of different parameters associated with entropy variation-based detection of DDoS attacks. Three feature sets were used for studying the impact of entropy variation in detection. Further, we have analysed the three kinds of entropies used for the detection namely, Shannon entropy, generalized entropy, and ϕ-entropy. Along with that, threshold determination for entropy values have been done in two ways, i.e. [average−standarddeviation,average+standarddeviation] and [lower quartile, upper quartile]. We found that quartile-based threshold has generally given better recall and accuracy and more consistent results with respect to the window sizes. It is also found that ϕ-entropy has given overall better results as compared to the Shannon entropy and Generalized entropy. Therefore, a combination of ϕ-entropy and quartile-based threshold is found to be the best parameters to identify the DDoS attacks.

Research on Intrusion Detection Technology of Computing Nodes in Digital Power Grid based on Artificial Intelligence

This paper aims to investigate an intrusion detection network for digital power grid networks, which consists of an edge server and two computational nodes that work collaboratively to detect any potential intrusion in the network. The primary objective of this study is to enhance the effectiveness of intrusion detection in the network. To achieve this objective, we first define the outage probability of the intrusion detection system under consideration. This is done to provide a measure of the probability that the system fails to detect an intrusion when it occurs. We then derive a closed-form expression for the outage probability to enable further analysis on the system behavior. Since the system resources, such as transmit power, are limited, we further design a transmit power allocation strategy to improve the system performance. This strategy seeks to optimize the allocation of transmit power across the different nodes of the intrusion detection network to maximize the likelihood of detecting intrusions while minimizing the resource usage. Finally, to evaluate the performance of the proposed system, we conduct simulations and provide results that demonstrate the accuracy of the closed-form expression and the effectiveness of the transmit power allocation strategy. These simulation results serve as evidence of the efficacy of the proposed approach in detecting intrusions in a resource-constrained network, especially for the digital power grid networks.

Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT.

The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers' attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patient's health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices'. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness system's integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy (99.31%) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.

Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks

Identifying vital nodes is important in disease research, spreading rumors, viral marketing, and drug development. The vital nodes in any network are used to spread information as widely as possible. Centrality measures such as Degree centrality (D), Betweenness centrality (B), Closeness centrality (C), Katz (K), Cluster coefficient (CC), PR (PageRank), LGC (Local and Global Centrality), ISC (Isolating Centrality) centrality measures can be used to effectively quantify vital nodes. The majority of these centrality measures are defined in the literature and are based on a network’s local and/or global structure. However, these measures are time-consuming and inefficient for large-scale networks. Also, these measures cannot study the effect of removal of vital nodes in resource-constrained networks. To address these concerns, we propose the six new centrality measures namely GRACC, LRACC, GRAD, LRAD, GRAK, and LRAK. We develop these measures based on the relative change of the clustering coefficient, degree, and Katz centralities after the removal of a vertex. Next, we compare the proposed centrality measures with D, B, C, CC, K, PR, LGC, and ISC to demonstrate their efficiency and time complexity. We utilize the SIR (Susceptible-Infected-Recovered) and IC (Independent Cascade) models to study the maximum information spread of proposed measures over conventional ones. We perform extensive simulations on large-scale real-world data sets and prove that local centrality measures perform better in some networks than global measures in terms of time complexity and information spread. Further, we also observe the number of cliques drastically improves the efficiency of global centrality measures.

Computational Overhead Analysis for Mitigation of Content Poisoning Attack in NDN-based IoT Body Area Networks

The Body Sensors Network has developed into a significant element of the Medical IoT. Massive data gathering from a resource-constrained network represents a laborious procedure in IoT equipped BSN/WBAN, creating a number of difficult problems. Conventional communication protocols may be used on IoT platforms without any alterations and provide impractical techniques for large-scale networks. The new architecture known as ICN has the potential to overcome these difficulties in huge data collection. According to this perspective, the proposed work indicates a CPA mitigation strategy for information centric WBANs.

Genetic Algorithm for Cross-Layer-Based Energy Hole Minimization in Wireless Sensor Networks

Wireless sensor networks encompass wireless connectivity, low energy consumption protocols, and many algorithms. The sensor node battery is marked as a striking problem in such resource-constrained networks. Once the sensor nodes are mounted in an area, the batteries present in nodes are difficult to repair or replace. Many algorithms and protocols are implemented to progressively minimize the usage of energy in nodes. The sensor in charge of scheduling packets is significantly essential to reduce the processing overhead, energy consumption. Maximum-largest weighted delay first (M-LWDF) and packet dependent (PD) scheduling algorithms have been investigated in the multiuser multiservice systems. Efficiency and performance are better especially for multiuser multiservice to nonloss transmission during resource lag. Proposed work of cross-layer approach using genetic algorithm improves energy quality and efficiency by 24.7% compared to the existing methods and finds useful applications are low.

Data-driven control for switched systems over a vulnerable and resource-constrained network

To alleviate the restriction of system model on control design, data-driven model-free adaptive control (MFAC) is an excellent alternative to model-based control methods. This paper studies event-triggered data-driven control for switched systems over a vulnerable and resource-constrained network. The system is transformed into an equivalent switched data model through dynamic linearization. Resource constraints and denial of service (DoS) attacks in the network are concerned, and a novel joint anti-attack method including resilient event-triggering mechanism and prediction scheme is presented. Furthermore, new event-triggered MFAC algorithms are proposed. In this scenario, by constructing a Lyapunov functional on tracking error, sufficient conditions to ensure its boundedness are derived. This is the first time in the literature to give a complete solution to data-driven control of switched systems. At last, the validity of new algorithms and theoretical results is confirmed by simulations.